International Journal of Trend in Scientific
Research and Development (IJTSRD)
International Open Access Journal
ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume - 2 | Issue – 5
Drought Assessment Using Standard Precipitation Index
Aye Aye Thant
Lecturer, Department of Civil Engineering, Mandalay Technological University,
Patheingyi, Mandalay, Myanmar
ABSTRACT
Drought is one of the extreme climatic events among
the most relevant natural disasters. This paper
presents drought assessment using Standardized
Precipitation Index (SPI) to overview the respective
drought hot spots. Monthly rainfall data during the
previous 36 years (1982 – 2017) are applied to
generate Standardized Precipitation Index (SPI) with
3-month and 9-month scale on the basis of Gamma
distribution for the areas in the central dry zone of
Myanmar namely Mandalay, Nyaung-U, Myingyan,
Natogyi, Meikhtila, Kyaukpadaung, Wundwin,
Sagaing, Monywa, Shwebo, Myinmu, Magway,
Minbu, Chauk and Pakokku. The generated SPI
values for different time scale are classified to assess
drought hot spots for the central dry zone, Myanmar
as an areal extent of annual drought.
Keywords:
Monthly
Rainfall,
Standardized
Precipitation Index (SPI), Drought Assessment.
I.
INTRODUCTION
Global warming is likely to alter patterns of global air
circulation and hydrologic cycle that will change
global and regional rainfall regimes. The amount and
timing of runoff and intensity of floods and droughts
are directly affected by a change in the total amount,
frequency and the intensity of rainfall. In addition,
extreme climate events such as drought are expected
to become more frequent and intense in the world [1].
Drought which is an extreme event, is a natural hazard
related to a prolonged lack of rainfall that leads to a
temporary decrease or deficit in natural water
availability. Unlike other disasters such as floods,
tornados, tropical cyclones, earthquakes or volcanic
eruptions, drought is a slowly developing
phenomenon as it propagates through the full
hydrologic cycle and it often shows persistent
consequences after its termination [2].
The central dry zone in Myanmar is one of the most
climate sensitive and natural resources poor regions.
The dry zone lies stretching across the southern part
of Sagaing Region, the western and middle part of
Mandalay Region and most parts of Magway Region.
This zone represents about 10% of the country’s total
land area and about 34% of the country’s total
population live in this zone. The area of dry zone is
characterized by low rainfall, intense heat and
degraded soil conditions all of which affecting social
and economic situations of the communities living in
this region. According to the Annual Drought Reports
(2010–2013) prepared by Drought Monitoring Centre
of Department of Meteorology and Hydrology
(DMH), central area of Myanmar is more vulnerable
to drought as compared to other parts. Intense rainfall
frequency and intensity in this zone are scarcely
occurred and temperature rises mostly above 40°C.
The annual rainfall in this dry zone is less than
750mm, while the national average rainfall is
2353mm. In most areas of this zone, economic
damages caused by droughts are as greater as those
caused by other natural disasters. Prolonged drought
cycles are major factors in land degradation processes
in this zone. The dangers of drought are liable to lead
a major crisis in water resources and affect all sectors
of society [3].
II. MATERIALS AND METHOD
Monthly rainfall records for the period from 1982 to
2017 were collected from the Department of
Meteorology and Hydrology for 15 selected stations
in central dry zone of Myanmar namely Mandalay,
Nyaung-U,
Myingyan,
Natogyi,
Meikhtila,
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Kyaukpadaung, Wundwin, Sagaing, Monywa,
Shwebo, Myinmu, Magway, Minbu, Chauk and
Pakokku. The SPI based on the Gamma distribution,
developed by McKee, Doesken and Kleist (1993) has
been generated using spi_sl_6.exe available in
Windows/PC
version
published
by
World
Meteorological Organization, 2012. The SPI was
based on the probability of rainfall for any timescale.
The probability of observed rainfall was then
transformed into an index. SPI describes the actual
rainfall as a standardized departure with respect to
rainfall probability distribution function and hence the
index has gained importance in recent years as a
potential drought indicator across different stations. In
this paper, the SPI values have been generated for 3month and 9-month timescales.
A. Gamma Distribution Function
Computing of SPI involves fitting a Gamma
probability density function to a given frequency
distribution of rainfall for a station. The SPI
represents a Z-score used in statistics. Thom (1966)
found the Gamma distribution to fit climatological
precipitation time series well [6]. The Gamma
distribution is defined by its frequency or probability
density function:
1
-x
g(x) = βα τ(α) xα - 1 e ⁄β
Where α and β be the parameters of the Gamma
probability density function which are estimated for
each station and each timescale of interest the year.
The maximum likelihood solutions are used to
optimally estimate α and β:
1
α = 4 A (1+ √1+
4A
3
)
x
β =α
Where:
ln⁡(x)
A = ln(x) - ∑ n
n = number of rainfall observations
Resulting parameters are then used to find the
cumulative probability of an observed precipitation
event for the given month and time scale for the
station.
Cumulative probability is given by:
x
x
1
-x
G(x) = ∫0 g(x)dx βατ(α) ∫0 xα - 1 e ⁄β dx
Letting t = "x" /"β" be the equation becomes complete
function:
t
1
G(x) = τ(α) ∫0 tα - 1 et dt
Since the Gamma distribution function is undefined
for x = 0 and the precipitation distribution may
contain zeros, the cumulative probability becomes:
H(x) = q + (1 – q) G(x)
Where, q is the probability of a zero. The cumulative
probability H(x) is then transformed to standard
normal random variable Z with a mean zero and
variance of one, which is the value of the SPI.
For 0 < H(x) ≤ 0.5,
Z = SPI = (t-
c0 + c1 t + c2 t2
1+ d1 t+ d2 t2 + d3 t3
)
For 0.5 < H(x) ≤ 1.0,
Z = SPI = (t-
c0 + c1 t + c2 t2
1+ d1 t+ d2 t2 + d3 t3
)
Where,
1
t = √ln ((H(x))2 )for 0 < H(x) ≤ 0.5
1
t = √ln ((1 - H(x))2)for 0.5 < H(x) ≤ 1.0
c0 = 2.515517
c1 = 0.802853
c2 = 0.010328
d1 = 1.432788
d2 = 0.189269
d3 = 0.001308
B. Program of spi_sl_6.exe
This application is a statistical monthly indicator
during a period of n months with the long-term
cumulated rainfall distribution for the same location
and accumulation period. Rainfall is the only input
parameter. Ideally, one needs at least 20-30 years of
monthly values, with 50-60 years (or more) being
optimal and preferred. The program can be run with
missing data, but it will affect the confidence of the
results, depending on the distribution of the missing
data in relation to the length of the record. The
application is a tool which was developed primarily
for defining and monitoring drought but is not a
drought prediction tool. Because of computer aid
application, the input data is simply defined on
Microsoft Notepad as “.cor”. This data is saved as
MS_DOS ASCII text files. It can generate with multitimescales in one calculation whereas output file must
have “.dat” format. The results for one event has in
same folder with spi_sl_6.exe.[4] Observed SPIs from
the application can be plotted in Excel spreadsheet.
C. SPI Timescales
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
The SPI was designed to quantify the rainfall deficit
for multiple timescales: namely, 1-month, 3-month, 6month, 9-month, 12-monht up to 24-month
timescales. In this study, 3-month and 9-month
timescales of SPI are generated and analyzed.
The 3-month SPI provides a comparison of the
rainfall over a specific 3-month period with the
rainfall totals from the same 3-month period for all
the years included in the historical record. A 3-month
SPI reflects a short-term and medium-term moisture
conditions and provides a seasonal estimation of
precipitation. Because these periods are characterized
by little rain, the corresponding historical totals will
be small, and relatively small deviations on either side
of the mean could result in large negative or positive
SPIs [4, 5].
The 9-month SPI provides an indication of interseasonal precipitation patterns over a medium
timescale duration. Drought usually takes a season or
more to develop. SPI values below -1.5 for these
timescales are usually a good indication that dryness
is having a significant impact on agriculture and may
be affecting other sectors as well. This time period
begins to bridge a short-term seasonal drought to
those longer-term droughts that may become
hydrological or multi-year, in nature [4, 5].
D. Classification of Drought Event
McKee and others (1993) used the classification
system in the SPI value table (Table 1) to define
drought intensities resulting from the SPI. They
defined the criteria for a drought event for any of the
timescales. A drought event occurs any time when the
SPI is continuously negative and reaches at intensity
of -1.0 or less. The event ends when the SPI becomes
positive. Each drought event has a duration defined by
its beginning and end, and the intensity for each
month that the event continues [4]. The drought
categories are classified based on SPI values as shown
in Table 1.
Table1. Drought Classification in SPI Values
SPI Values Drought Category
≥ 2.0
Extremely wet
1.5 to 1.99
Very wet
1.0 to 1.49
Moderately wet
-0.99 to 0.99
Near normal
-1.0 to -1.49
Moderately dry
-1.5 to -1.99
Severely dry
< -2.0
Extremely dry
III. Results and Discussion
The results of cohesion soil are as follows.SPI for 3month and 9-month timescales are generated adopting
the methodology mentioned above, and drought
categories are classified. Figures 1 through 12 are
illustrations of SPI-3 and SPI-9 for some selected
stations in central dry zone. Figure 13 and Figure 14
present drought occurrence in percentage for both SPI
timescales for every station under study. It is evident
from these figures that moderate droughts were
occurred frequently in all stations. Extreme drought
did not occur at Pakokku in SPI-3 time scale. But
other stations experienced extreme drought for both
timescales in 1983. The most extreme droughts were
occurred in 1983 (May, June and December), 2004
(December) and 2005 (May) for 3-month timescale
and 1983 (March, April, May and June), 2005 (June)
for 9-month timescale, where most regions
experienced droughts less than -2, including some
regions facing very extreme drought with SPI values
less than -3.1. The areas which experienced severe
drought for both SPI timescales were observed as
Mandalay, Nyaung-U, Myingyan, Kyaukpadaung,
Sagaing, Monywa, Myinmu, Magway, Minbu and
Pakokku. The extreme droughts less than -2.4 were
found in Mandalay (December, 2004), Nyaung-U
(August, 1998), Kyaukpadaung (May, 2005),
Magway (July, 1985) for SPI-3 timescale and
Myingyan (July, 1983), Wundwin (June, 1993),
Sagaing (November, 1994), Monywa (December,
1982) for SPI-9 timescale. Though, for SPI-9 time
scale, less frequent extremely drought was found in
Minbu and more moderate wet years were observed in
Chauk.
Figure 15 illustrates areal extent of annual droughts
for both SPI timescales during 1982-2017. As for
areal extent of annual drought in SPI-3 during 19822017, only moderate drought was occurred at three
stations (Nyaung-U, Monywa and Pakokku) in 1996,
two stations (Monywa and Shwebo) in 2002, one
station (Shwebo) in 2006, one station (Nyaung-U) in
2017 and, drought did not occur at any stations in
2011 among selected stations. As for areal extent of
annual drought in SPI-9 during 1982-2017, only
moderate drought was occurred at two stations
(Myingyan and Magway) in 1984, three stations
(Sagaing, Monywa and Shwebo) in 2002, two stations
(Meikhtila and Wundwin) in 2003, four stations
(Natogyi, Wundwin, Myinmu and Minbu) in 2004
and, drought did not occur at any stations in 2007,
2011, 2016 and 2017 among selected stations.
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Fig1. SPI-3 for Mandalay
Fig5. SPI-3 for Magway
Fig6. SPI-3 for Pakokku
Fig2. SPI-3 for Nyaung-U
Fig 3.SPI-3 for Kyaukpadaung
Fig7. SPI-9 for Myingyan
Fig4. SPI-3 for Myinmu
Fig8. SPI-9 for Wundwin
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Fig9. SPI-9 for Sagaing
Fig11. SPI-9 for Minbu
Fig10. SPI-9 for Monywa
Fig12. SPI-9 for Chauk
Fig13. Percentage of Drought Occurrence in SPI-3
Fig14. Percentage of Drought Occurrence in SPI-9
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Fig15. Areal Extent of Annual Droughts in SPI-3
IV. Conclusion Remark
As a conclusion, drought affects virtually all studied
areas in central dry zone and more than one half of the
zone is susceptible to drought almost every year and,
this study can be used as a general reference for
enhancing their early warning capabilities for drought.
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Variation in Dry Zone of Myanmar”, 2016.
4. World
Meteorological
Organization,
“Standardized Precipitation Index User Guide”,
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5. Jaber Almedeij, “Drought Analysis for Kuwait
Using Standardized Precipitation Index”, 2014.
6. James E. CaskeyJr, “A note on the Gamma
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